class: title-slide background-image: url("https://raw.githubusercontent.com/garthtarr/sydney_xaringan/master/assets/USydLogo-white.svg"), url("https://raw.githubusercontent.com/garthtarr/sydney_xaringan/master/assets/title-image1.jpg") background-position: 10% 90%, 100% 50% background-size: 160px, 50% 100% background-color: #0148A4 # .text-shadow[.white[STAT5003]] ## .white[Week 1: Basics of R programming] ## .white[& static graphics] ### .white[Dr. Justin Wishart] ### .white[Semester 2, 2020] --- ## Aims of this course
* Learn .brand-red[concepts] and .brand-red[methods] related to statistical data analysis and statistical learning * Apply the methods on .brand-red[real datasets] in <svg style="height:100;fill:steelblue;" viewBox="0 0 581 512"><path d="M581 226.6C581 119.1 450.9 32 290.5 32S0 119.1 0 226.6C0 322.4 103.3 402 239.4 418.1V480h99.1v-61.5c24.3-2.7 47.6-7.4 69.4-13.9L448 480h112l-67.4-113.7c54.5-35.4 88.4-84.9 88.4-139.7zm-466.8 14.5c0-73.5 98.9-133 220.8-133s211.9 40.7 211.9 133c0 50.1-26.5 85-70.3 106.4-2.4-1.6-4.7-2.9-6.4-3.7-10.2-5.2-27.8-10.5-27.8-10.5s86.6-6.4 86.6-92.7-90.6-87.9-90.6-87.9h-199V361c-74.1-21.5-125.2-67.1-125.2-119.9zm225.1 38.3v-55.6c57.8 0 87.8-6.8 87.8 27.3 0 36.5-38.2 28.3-87.8 28.3zm-.9 72.5H365c10.8 0 18.9 11.7 24 19.2-16.1 1.9-33 2.8-50.6 2.9v-22.1z"/></svg> (the statistical computing language) * Aim is to teach you to use the methods, not necessarily to go into all the mathematical details --- ## Basics of Statistical Computing * Review basic statistical concepts * Introduction to <a href="https://www.r-project.org/"><svg style="height:50;fill:steelblue;" viewBox="0 0 581 512"><path d="M581 226.6C581 119.1 450.9 32 290.5 32S0 119.1 0 226.6C0 322.4 103.3 402 239.4 418.1V480h99.1v-61.5c24.3-2.7 47.6-7.4 69.4-13.9L448 480h112l-67.4-113.7c54.5-35.4 88.4-84.9 88.4-139.7zm-466.8 14.5c0-73.5 98.9-133 220.8-133s211.9 40.7 211.9 133c0 50.1-26.5 85-70.3 106.4-2.4-1.6-4.7-2.9-6.4-3.7-10.2-5.2-27.8-10.5-27.8-10.5s86.6-6.4 86.6-92.7-90.6-87.9-90.6-87.9h-199V361c-74.1-21.5-125.2-67.1-125.2-119.9zm225.1 38.3v-55.6c57.8 0 87.8-6.8 87.8 27.3 0 36.5-38.2 28.3-87.8 28.3zm-.9 72.5H365c10.8 0 18.9 11.7 24 19.2-16.1 1.9-33 2.8-50.6 2.9v-22.1z"/></svg></a> * Reproducible coding using Rmarkdown <img src=https://github.com/rstudio/hex-stickers/blob/master/PNG/rmarkdown.png?raw=true width="50px" /> * Recommended IDE via <a href="https://rstudio.com/products/rstudio/"><img src=https://rstudio.com/wp-content/uploads/2018/10/RStudio-Logo-flat.svg width="100px" /></a> --- class: segue-yellow background-image: url("https://raw.githubusercontent.com/garthtarr/sydney_xaringan/master/assets/USydLogo.svg") background-size: 20% background-position: 95% 95% # Review of basic statistical concepts --- ## Population * __Definition__: - The set of data (numeric or otherwise) corresponding to the entire collection of units about which information is sought. * __Examples__: - Blood pressure – Blood pressure readings of ALL people in Australia. - The number of languages spoken from ALL currently enrolled students in University of Sydney --- ## Sample * __Definition:__ - A subset of the population data that are actually collected in the course of a study. * __Examples__: - Blood pressure readings of 1000 randomly selected people in Australia. - The number of languages spoken from 500 randomly selected students currently enrolled in University of Sydney. > In most studies, it is difficult to obtain information about the whole population. That is why we rely on samples to make estimates and inferences related to the whole population. --- ## Parameters vs statistics * A .brand-red[parameter] is a number that describes a population. - Notation usually denoted with Greek letters. e.g. `\(\mu\)`, `\(\sigma\)` * A .brand-red[statistic] is a number that describes a sample. - Sample statistics are usually denoted using Roman letters, e.g. `\(x\)`, `\(s\)`. * A parameter is a fixed number (usually unknown). A statistic is a variable whose value varies from sample to sample. --- ## Descriptive statistics – numeric and graphics Many methods are available for summarising data in both **numeric** and **graphical** form Numeric measures: * Measure of location – Mean, Median, Mode for numeric data - Counts, proportions for categorical data * Measure of spread – Standard deviation, MAD (median absolute deviation), IQR * Others: – Min, Max, Quartile, Five number summaries (used later in boxplot) --- class: segue-yellow background-image: url("https://raw.githubusercontent.com/garthtarr/sydney_xaringan/master/assets/USydLogo.svg") background-size: 20% background-position: 95% 95% # Basic statistical graphics <svg style="height:0.8em;top:.04em;position:relative;" viewBox="0 0 581 512"><path d="M581 226.6C581 119.1 450.9 32 290.5 32S0 119.1 0 226.6C0 322.4 103.3 402 239.4 418.1V480h99.1v-61.5c24.3-2.7 47.6-7.4 69.4-13.9L448 480h112l-67.4-113.7c54.5-35.4 88.4-84.9 88.4-139.7zm-466.8 14.5c0-73.5 98.9-133 220.8-133s211.9 40.7 211.9 133c0 50.1-26.5 85-70.3 106.4-2.4-1.6-4.7-2.9-6.4-3.7-10.2-5.2-27.8-10.5-27.8-10.5s86.6-6.4 86.6-92.7-90.6-87.9-90.6-87.9h-199V361c-74.1-21.5-125.2-67.1-125.2-119.9zm225.1 38.3v-55.6c57.8 0 87.8-6.8 87.8 27.3 0 36.5-38.2 28.3-87.8 28.3zm-.9 72.5H365c10.8 0 18.9 11.7 24 19.2-16.1 1.9-33 2.8-50.6 2.9v-22.1z"/></svg> and <img src = https://github.com/rstudio/hex-stickers/blob/master/PNG/ggplot2.png?raw=true width = 50px/> --- ## Types of graphics covered in this course * Become familiar with simple graphics using base <svg style="height:0.8em;top:.04em;position:relative;fill:steelblue;" viewBox="0 0 581 512"><path d="M581 226.6C581 119.1 450.9 32 290.5 32S0 119.1 0 226.6C0 322.4 103.3 402 239.4 418.1V480h99.1v-61.5c24.3-2.7 47.6-7.4 69.4-13.9L448 480h112l-67.4-113.7c54.5-35.4 88.4-84.9 88.4-139.7zm-466.8 14.5c0-73.5 98.9-133 220.8-133s211.9 40.7 211.9 133c0 50.1-26.5 85-70.3 106.4-2.4-1.6-4.7-2.9-6.4-3.7-10.2-5.2-27.8-10.5-27.8-10.5s86.6-6.4 86.6-92.7-90.6-87.9-90.6-87.9h-199V361c-74.1-21.5-125.2-67.1-125.2-119.9zm225.1 38.3v-55.6c57.8 0 87.8-6.8 87.8 27.3 0 36.5-38.2 28.3-87.8 28.3zm-.9 72.5H365c10.8 0 18.9 11.7 24 19.2-16.1 1.9-33 2.8-50.6 2.9v-22.1z"/></svg> and ggplot graphics <img src = https://github.com/rstudio/hex-stickers/blob/master/PNG/ggplot2.png?raw=true width = 50px/> * base <svg style="height:0.8em;top:.04em;position:relative;fill:steelblue;" viewBox="0 0 581 512"><path d="M581 226.6C581 119.1 450.9 32 290.5 32S0 119.1 0 226.6C0 322.4 103.3 402 239.4 418.1V480h99.1v-61.5c24.3-2.7 47.6-7.4 69.4-13.9L448 480h112l-67.4-113.7c54.5-35.4 88.4-84.9 88.4-139.7zm-466.8 14.5c0-73.5 98.9-133 220.8-133s211.9 40.7 211.9 133c0 50.1-26.5 85-70.3 106.4-2.4-1.6-4.7-2.9-6.4-3.7-10.2-5.2-27.8-10.5-27.8-10.5s86.6-6.4 86.6-92.7-90.6-87.9-90.6-87.9h-199V361c-74.1-21.5-125.2-67.1-125.2-119.9zm225.1 38.3v-55.6c57.8 0 87.8-6.8 87.8 27.3 0 36.5-38.2 28.3-87.8 28.3zm-.9 72.5H365c10.8 0 18.9 11.7 24 19.2-16.1 1.9-33 2.8-50.6 2.9v-22.1z"/></svg> use the built in plotting functions - typically good for quick plots of simple datasets * ggplot graphics <img src = https://github.com/rstudio/hex-stickers/blob/master/PNG/ggplot2.png?raw=true width = 50px/> - Name meaning the grammar of graphics - Typically better for more complicated datasets * Not covered but honorable mention <img src=https://www.vectorlogo.zone/logos/plot_ly/plot_ly-ar21.svg height=50px /> - Plotly is a powerful plotting library - Can do interactive graphics --- ## Simple example dataframe for plots ```r example.dat <- data.frame(x = rnorm(100), y = runif(100), cat = sample(LETTERS[1:2], prob = c(1, 3), size = 100, replace = TRUE)) head(example.dat) ``` ``` ## x y cat ## 1 -1.01720533 0.53678627 B ## 2 0.66728073 0.90411240 B ## 3 -0.34239565 0.10107771 B ## 4 -0.80917484 0.03485421 A ## 5 -0.17392371 0.08812804 B ## 6 0.01421694 0.35850014 B ``` --- ## Single numeric variable: Boxplot in base <svg style="height:0.8em;top:.04em;position:relative;fill:steelblue;" viewBox="0 0 581 512"><path d="M581 226.6C581 119.1 450.9 32 290.5 32S0 119.1 0 226.6C0 322.4 103.3 402 239.4 418.1V480h99.1v-61.5c24.3-2.7 47.6-7.4 69.4-13.9L448 480h112l-67.4-113.7c54.5-35.4 88.4-84.9 88.4-139.7zm-466.8 14.5c0-73.5 98.9-133 220.8-133s211.9 40.7 211.9 133c0 50.1-26.5 85-70.3 106.4-2.4-1.6-4.7-2.9-6.4-3.7-10.2-5.2-27.8-10.5-27.8-10.5s86.6-6.4 86.6-92.7-90.6-87.9-90.6-87.9h-199V361c-74.1-21.5-125.2-67.1-125.2-119.9zm225.1 38.3v-55.6c57.8 0 87.8-6.8 87.8 27.3 0 36.5-38.2 28.3-87.8 28.3zm-.9 72.5H365c10.8 0 18.9 11.7 24 19.2-16.1 1.9-33 2.8-50.6 2.9v-22.1z"/></svg> ```r boxplot(example.dat$x) ``` <img 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width="70%" /> --- ## Single numeric variable: Boxplot in <img src = https://github.com/rstudio/hex-stickers/blob/master/PNG/ggplot2.png?raw=true width = 50px/> ```r library(ggplot2) # Only need to load the library once in an R session ggplot(example.dat, aes(y = x)) + geom_boxplot() + theme_minimal() ``` <img src="data:image/svg+xml;base64,<?xml version='1.0' encoding='UTF-8' ?>
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" width="70%" /> --- ## Single numeric variable: Histogram in base <svg style="height:0.8em;top:.04em;position:relative;fill:steelblue;" viewBox="0 0 581 512"><path d="M581 226.6C581 119.1 450.9 32 290.5 32S0 119.1 0 226.6C0 322.4 103.3 402 239.4 418.1V480h99.1v-61.5c24.3-2.7 47.6-7.4 69.4-13.9L448 480h112l-67.4-113.7c54.5-35.4 88.4-84.9 88.4-139.7zm-466.8 14.5c0-73.5 98.9-133 220.8-133s211.9 40.7 211.9 133c0 50.1-26.5 85-70.3 106.4-2.4-1.6-4.7-2.9-6.4-3.7-10.2-5.2-27.8-10.5-27.8-10.5s86.6-6.4 86.6-92.7-90.6-87.9-90.6-87.9h-199V361c-74.1-21.5-125.2-67.1-125.2-119.9zm225.1 38.3v-55.6c57.8 0 87.8-6.8 87.8 27.3 0 36.5-38.2 28.3-87.8 28.3zm-.9 72.5H365c10.8 0 18.9 11.7 24 19.2-16.1 1.9-33 2.8-50.6 2.9v-22.1z"/></svg> ```r hist(example.dat$x) ``` <img 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width="70%" /> --- ## Single numeric variable: Histogram in <img src = https://github.com/rstudio/hex-stickers/blob/master/PNG/ggplot2.png?raw=true width = 50px/> ```r ggplot(example.dat, aes(x = x)) + geom_histogram() + theme_minimal() ``` <img src="data:image/svg+xml;base64,<?xml version='1.0' encoding='UTF-8' ?>
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" width="70%" /> --- ## Two numeric variables: Scatterplot in <img src = https://github.com/rstudio/hex-stickers/blob/master/PNG/ggplot2.png?raw=true width = 50px/> ```r ggplot(example.dat, aes(x = x, y = y)) + geom_point() # default theme here ``` <img src="data:image/svg+xml;base64,<?xml version='1.0' encoding='UTF-8' ?>
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" width="70%" /> --- class: segue background-image: url("https://raw.githubusercontent.com/garthtarr/sydney_xaringan/master/assets/USydLogo-white.svg") background-size: 20% background-position: 95% 95% # A crash course in <svg style="height:100;fill:white;" viewBox="0 0 581 512"><path d="M581 226.6C581 119.1 450.9 32 290.5 32S0 119.1 0 226.6C0 322.4 103.3 402 239.4 418.1V480h99.1v-61.5c24.3-2.7 47.6-7.4 69.4-13.9L448 480h112l-67.4-113.7c54.5-35.4 88.4-84.9 88.4-139.7zm-466.8 14.5c0-73.5 98.9-133 220.8-133s211.9 40.7 211.9 133c0 50.1-26.5 85-70.3 106.4-2.4-1.6-4.7-2.9-6.4-3.7-10.2-5.2-27.8-10.5-27.8-10.5s86.6-6.4 86.6-92.7-90.6-87.9-90.6-87.9h-199V361c-74.1-21.5-125.2-67.1-125.2-119.9zm225.1 38.3v-55.6c57.8 0 87.8-6.8 87.8 27.3 0 36.5-38.2 28.3-87.8 28.3zm-.9 72.5H365c10.8 0 18.9 11.7 24 19.2-16.1 1.9-33 2.8-50.6 2.9v-22.1z"/></svg> --- ## What is <svg style="height:50;fill:steelblue;" viewBox="0 0 581 512"><path d="M581 226.6C581 119.1 450.9 32 290.5 32S0 119.1 0 226.6C0 322.4 103.3 402 239.4 418.1V480h99.1v-61.5c24.3-2.7 47.6-7.4 69.4-13.9L448 480h112l-67.4-113.7c54.5-35.4 88.4-84.9 88.4-139.7zm-466.8 14.5c0-73.5 98.9-133 220.8-133s211.9 40.7 211.9 133c0 50.1-26.5 85-70.3 106.4-2.4-1.6-4.7-2.9-6.4-3.7-10.2-5.2-27.8-10.5-27.8-10.5s86.6-6.4 86.6-92.7-90.6-87.9-90.6-87.9h-199V361c-74.1-21.5-125.2-67.1-125.2-119.9zm225.1 38.3v-55.6c57.8 0 87.8-6.8 87.8 27.3 0 36.5-38.2 28.3-87.8 28.3zm-.9 72.5H365c10.8 0 18.9 11.7 24 19.2-16.1 1.9-33 2.8-50.6 2.9v-22.1z"/></svg>? * Free, open source software designed for statistical computing * Runs on Windows, Mac, Linux and other flavours of Unix * Provides an interactive environment, but it is also an interpreted programming language * Its power lies in the thousands of contributed packages on --- ## Base <svg style="height:0.8em;top:.04em;position:relative;fill:steelblue;" viewBox="0 0 581 512"><path d="M581 226.6C581 119.1 450.9 32 290.5 32S0 119.1 0 226.6C0 322.4 103.3 402 239.4 418.1V480h99.1v-61.5c24.3-2.7 47.6-7.4 69.4-13.9L448 480h112l-67.4-113.7c54.5-35.4 88.4-84.9 88.4-139.7zm-466.8 14.5c0-73.5 98.9-133 220.8-133s211.9 40.7 211.9 133c0 50.1-26.5 85-70.3 106.4-2.4-1.6-4.7-2.9-6.4-3.7-10.2-5.2-27.8-10.5-27.8-10.5s86.6-6.4 86.6-92.7-90.6-87.9-90.6-87.9h-199V361c-74.1-21.5-125.2-67.1-125.2-119.9zm225.1 38.3v-55.6c57.8 0 87.8-6.8 87.8 27.3 0 36.5-38.2 28.3-87.8 28.3zm-.9 72.5H365c10.8 0 18.9 11.7 24 19.2-16.1 1.9-33 2.8-50.6 2.9v-22.1z"/></svg> and the <img src="assets/tidyverse.png" width=50px/> * The `tidyverse` popularised by Hadley Wickham and the team at <a href="https://rstudio.com/products/rstudio/"><img src=https://rstudio.com/wp-content/uploads/2018/10/RStudio-Logo-flat.svg width="100px" /></a> - Has a (somewhat) standardised syntax (pipes `%>%` are king except for `+` in `ggplot2`) - Produces more human readable code - Not as stable as base, breaking changes occur as `tidyverse` develops. - Good for interactive data analyst * Core base <svg style="height:0.8em;top:.04em;position:relative;fill:steelblue;" viewBox="0 0 581 512"><path d="M581 226.6C581 119.1 450.9 32 290.5 32S0 119.1 0 226.6C0 322.4 103.3 402 239.4 418.1V480h99.1v-61.5c24.3-2.7 47.6-7.4 69.4-13.9L448 480h112l-67.4-113.7c54.5-35.4 88.4-84.9 88.4-139.7zm-466.8 14.5c0-73.5 98.9-133 220.8-133s211.9 40.7 211.9 133c0 50.1-26.5 85-70.3 106.4-2.4-1.6-4.7-2.9-6.4-3.7-10.2-5.2-27.8-10.5-27.8-10.5s86.6-6.4 86.6-92.7-90.6-87.9-90.6-87.9h-199V361c-74.1-21.5-125.2-67.1-125.2-119.9zm225.1 38.3v-55.6c57.8 0 87.8-6.8 87.8 27.3 0 36.5-38.2 28.3-87.8 28.3zm-.9 72.5H365c10.8 0 18.9 11.7 24 19.2-16.1 1.9-33 2.8-50.6 2.9v-22.1z"/></svg> - Good for production level code - Stable - Function syntax inconsistent --- ## What is <a href="https://rstudio.com/products/rstudio/"><img src=https://rstudio.com/wp-content/uploads/2018/10/RStudio-Logo-flat.svg width="100px" /></a>? * RStudio is an integrated development environment (IDE) for <svg style="height:50;fill:bluesteel;" viewBox="0 0 581 512"><path d="M581 226.6C581 119.1 450.9 32 290.5 32S0 119.1 0 226.6C0 322.4 103.3 402 239.4 418.1V480h99.1v-61.5c24.3-2.7 47.6-7.4 69.4-13.9L448 480h112l-67.4-113.7c54.5-35.4 88.4-84.9 88.4-139.7zm-466.8 14.5c0-73.5 98.9-133 220.8-133s211.9 40.7 211.9 133c0 50.1-26.5 85-70.3 106.4-2.4-1.6-4.7-2.9-6.4-3.7-10.2-5.2-27.8-10.5-27.8-10.5s86.6-6.4 86.6-92.7-90.6-87.9-90.6-87.9h-199V361c-74.1-21.5-125.2-67.1-125.2-119.9zm225.1 38.3v-55.6c57.8 0 87.8-6.8 87.8 27.3 0 36.5-38.2 28.3-87.8 28.3zm-.9 72.5H365c10.8 0 18.9 11.7 24 19.2-16.1 1.9-33 2.8-50.6 2.9v-22.1z"/></svg> * Think of it as the front-end, like a powerful text editor * R needs to be installed first before RStudio. --- ## <svg style="height:0.8em;top:.04em;position:relative;fill:steelblue;" viewBox="0 0 581 512"><path d="M581 226.6C581 119.1 450.9 32 290.5 32S0 119.1 0 226.6C0 322.4 103.3 402 239.4 418.1V480h99.1v-61.5c24.3-2.7 47.6-7.4 69.4-13.9L448 480h112l-67.4-113.7c54.5-35.4 88.4-84.9 88.4-139.7zm-466.8 14.5c0-73.5 98.9-133 220.8-133s211.9 40.7 211.9 133c0 50.1-26.5 85-70.3 106.4-2.4-1.6-4.7-2.9-6.4-3.7-10.2-5.2-27.8-10.5-27.8-10.5s86.6-6.4 86.6-92.7-90.6-87.9-90.6-87.9h-199V361c-74.1-21.5-125.2-67.1-125.2-119.9zm225.1 38.3v-55.6c57.8 0 87.8-6.8 87.8 27.3 0 36.5-38.2 28.3-87.8 28.3zm-.9 72.5H365c10.8 0 18.9 11.7 24 19.2-16.1 1.9-33 2.8-50.6 2.9v-22.1z"/></svg> Session * __Working Directory__: Each session runs from a working directory - `getwd()` shows the current working directory for R. - `setwd(<path>)` to change the working directory where `<path>` is a string - E.g. `setwd("C:/Users/usyd-student")` for a windows user. * __Workspace__: Includes - Global environment: Data and variables loaded or defined - package environments: Any loaded packages with their functions/data. * __History__: - Can view your recent commands in the History pane - Can navigate previous commands using up and down arrows at your prompt --- ## Packages * Inspect the current environment - `sessionInfo()`: shows everything in the current R session. * To install a new package, - `install.packages("cluster")` will install the `cluster` package from the Comprehensive R Archive Network (CRAN) repository * To load a package to use in the environment `library(cluster)` - After loading a package, you will be able to use the functions provided in that package * If name conflicts arise you can specify the desired function using `::` - E.g. `dplyr::filter` will use the `filter` function from the `dplyr` package instead of the `stats::filter` function. --- ## Help * CRAN requires every exported function in every contributed package to have a help file - `help.start()` - `help(plot)` - `? plot` * In general, the help file for each function gives a brief description of what the function does, the required inputs, the expected outputs and some examples - Some packages also include a "vignette", a short document with guidance on using the package. --- ## Quirks of R syntax * `<-` is the symbol for ‘assign’ - Example: `x <- 14` - which is equivalent to: `x = 14` when used at the prompt * Should use `=` for argument matching in a function * The period symbol `.` can be used in variable names - Example: `new.vector <- c("A", "B", "C")` * Element indexing starts at 1 ```r new.vector <- c("A", "B", "C") new.vector[0] ``` ``` ## character(0) ``` ```r new.vector[1] ``` ``` ## [1] "A" ``` --- ## Basic data types in R .pull-left[ Classical data types * Numeric * Integer * Logical * Character * Complex ] .pull-right[ * Factor : Categorical data type - Unique to R (integer with some attributes) ```r data("ToothGrowth") levels(ToothGrowth$supp) ``` ``` ## [1] "OJ" "VC" ``` ```r class(ToothGrowth$supp) ``` ``` ## [1] "factor" ``` ```r str(ToothGrowth$supp) ``` ``` ## Factor w/ 2 levels "OJ","VC": 2 2 2 2 2 2 2 2 2 2 ... ``` ] --- ## Homogeneous vs non-homogenous data types in R ### Homogeneous * Vector - Sequence of data elements of the same basic data type * Matrix - Collection of data elements in a 2-dimensional array with rows and columns ### Non-homogeneous * List - More general structure containing other objects (including possibly other lists) * Data frame - Used for storing data, each column can be a different basic type - All columns must have the same length --- ## Vectors ```r new.vector <- c(1, 2, 3) class(new.vector) ``` ``` ## [1] "numeric" ``` ```r length(new.vector) ``` ``` ## [1] 3 ``` ```r new.vector[1:2] ``` ``` ## [1] 1 2 ``` ```r new.vector <- c(1, 2, "hello") class(new.vector) ``` ``` ## [1] "character" ``` --- ## Matrix ```r A <- matrix(c(2, 4, 3, 1, 7, 8), nrow = 3) # Unless specified otherwise, it will fill the matrix by column. A ``` ``` ## [,1] [,2] ## [1,] 2 1 ## [2,] 4 7 ## [3,] 3 8 ``` ```r A[2, 1] ``` ``` ## [1] 4 ``` ```r A[1, ] ``` ``` ## [1] 2 1 ``` ```r A[5] ``` ``` ## [1] 7 ``` --- ## List ```r vector.a <- c(1, 2, 3) vector.b <- c("hello", "world", "!!") new.list <- list(c(vector.a, vector.b)) new.list ``` ``` ## [[1]] ## [1] "1" "2" "3" "hello" "world" "!!" ``` ```r new.list <- list(vector.a, vector.b) new.list ``` ``` ## [[1]] ## [1] 1 2 3 ## ## [[2]] ## [1] "hello" "world" "!!" ``` ```r new.list[[1]] ``` ``` ## [1] 1 2 3 ``` --- ## Data frames .pull-left[ ```r head(warpbreaks) ``` ``` ## breaks wool tension ## 1 26 A L ## 2 30 A L ## 3 54 A L ## 4 25 A L ## 5 70 A L ## 6 52 A L ``` ```r class(warpbreaks) ``` ``` ## [1] "data.frame" ``` ```r head(warpbreaks$wool) ``` ``` ## [1] A A A A A A ## Levels: A B ``` ] .pull-right[ ```r str(warpbreaks) ``` ``` ## 'data.frame': 54 obs. of 3 variables: ## $ breaks : num 26 30 54 25 70 52 51 26 67 18 ... ## $ wool : Factor w/ 2 levels "A","B": 1 1 1 1 1 1 1 1 1 1 ... ## $ tension: Factor w/ 3 levels "L","M","H": 1 1 1 1 1 1 1 1 1 2 ... ``` ```r names(warpbreaks) ``` ``` ## [1] "breaks" "wool" "tension" ``` ] --- ## Rprojects * Each Rstudio project has its own working R session, workspace, history, and source documents * You can either create an Rproject in a new directory or within and existing directory * When you open an .Rproj file, the following happens: - A new R session (process) is started - If it exists, the .Rprofile in the project's main directory is loaded - If applicable, the .RData file in the main directory is loaded - If set, the .Rhistory file in the main directory is loaded - The current working directory is set to the project directory - If set, previously edited documents appear in the source editor.